runMDS {scater} | R Documentation |
Perform multi-dimensional scaling (MDS) on cells, based on the data in a SingleCellExperiment object.
runMDS(object, ncomponents = 2, ntop = 500, feature_set = NULL, exprs_values = "logcounts", scale_features = TRUE, use_dimred = NULL, n_dimred = NULL, method = "euclidean")
object |
A SingleCellExperiment object. |
ncomponents |
Numeric scalar indicating the number of MDS dimensions to obtain. |
ntop |
Numeric scalar specifying the number of most variable features to use for MDS. |
feature_set |
Character vector of row names, a logical vector or a numeric vector of indices indicating a set of features to use for MDS.
This will override any |
exprs_values |
Integer scalar or string indicating which assay of |
scale_features |
Logical scalar, should the expression values be standardised so that each feature has unit variance? |
use_dimred |
String or integer scalar specifying the entry of |
n_dimred |
Integer scalar, number of dimensions of the reduced dimension slot to use when |
method |
String specifying the type of distance to be computed between cells. |
The function cmdscale
is used internally to compute the multidimensional scaling components to plot.
Setting use_dimred
allows users to easily perform MDS on low-rank approximations of the original expression matrix (e.g., after PCA).
In such cases, arguments such as ntop
, feature_set
, exprs_values
and scale_features
will be ignored.
A SingleCellExperiment object containing the coordinates of the first ncomponent
MDS dimensions for each cell.
This is stored in the "MDS"
entry of the reducedDims
slot.
Aaron Lun, based on code by Davis McCarthy
## Set up an example SingleCellExperiment data("sc_example_counts") data("sc_example_cell_info") example_sce <- SingleCellExperiment( assays = list(counts = sc_example_counts), colData = sc_example_cell_info ) example_sce <- normalize(example_sce) example_sce <- runMDS(example_sce) reducedDimNames(example_sce) head(reducedDim(example_sce))